How to use a Gaussian Boson Sampler to learn from graph-structured data

          @misc{ scivideos_PIRSA:19070005,
            doi = {10.48660/19070005},
            url = {https://pirsa.org/19070005},
            author = {Schuld, Maria},
            keywords = {Quantum Matter},
            language = {en},
            title = {How to use a Gaussian Boson Sampler to learn from graph-structured data},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2019},
            month = {jul},
            note = {PIRSA:19070005 see, \url{https://scivideos.org/pirsa/19070005}}
          }
          

Abstract

A device called a ‘Gaussian Boson Sampler’ has initially been proposed as a near-term demonstration of classically intractable quantum computation. But these devices can also be used to decide whether two graphs are similar to each other. In this talk, I will show how to construct a feature map and graph similarity measure (or ‘graph kernel’) using samples from an optical Gaussian Boson Sampler, and how to combine this with a support vector machine to do machine learning on graph-structured datasets. I will present promising benchmarking results and try to motivate why such a continuous-variable quantum computer can actually extract interesting properties from graphs.